Comprehensive analysis of miRNA–mRNA interactions in ovaries of aged mice

Abstract Advanced maternal age and ovarian aging are deleterious to the quantity and quality of oocytes and epigenetic modifications, which can affect the health of offspring. However, relatively little is known about the regulation of microRNA‐mediated transcription during ovarian aging. We therefore aimed to identify age‐related mRNA and microRNA changes and their interactions in the ovaries of aged mice. We performed QuantSeq 3′mRNA and small RNA sequencing to compare their expression patterns in post‐ovulation ovaries from young (12‐week‐old) and old (44‐week‐old) mice. Functional annotation and integrative analyses were performed to identify the potential functions of differentially expressed genes and identify binding sites for critical microRNAs. We found 343 differentially expressed genes and 9 microRNAs in our comparison of the two mouse groups, with fold changes >2.0 (P < 0.01). Furthermore, we identified possible direct interactions between 24 differentially expressed mRNAs and 8 microRNAs. The differentially expressed genes are involved in fat digestion and absorption, the PI3K‐Akt signaling pathway, serotonergic synapse, and ovarian steroidogenesis, which are important for folliculogenesis and oocyte growth. During ovarian aging, changes in gene expression induce alterations in folliculogenesis, oocyte growth, and steroidogenesis, resulting in decreased oocyte quality and reproductive outcomes.

expression in aging ovaries. Many systematic approaches have revealed that gene regulation by epigenetic reprogramming occurs during folliculogenesis, oogenesis, and early embryogenesis (Bromfield et al., 2008;Zhang & Smith, 2015). Mammalian microRNAs (miRNAs) are small non-coding RNAs that regulate the post-transcriptional expression of target genes. miRNA-mediated post-transcriptional regulation plays important roles during many aspects of mammalian reproduction, ranging from the maturation of germ cells to the initiation of gastrulation Reza et al., 2019). In addition, several studies have examined the expression of miRNAs in aging organs of mice and humans (Gonzalo, 2010). Elucidating the molecular events underlying the aging process in ovaries is important for developing approaches to prevent female reproductive problems associated with age. However, little is known about how miRNAmediated transcription is regulated during ovarian aging.
In this study, we aimed to identify age-related mRNA and miRNA changes, and their interactions, in the ovaries of aged mice. The mice were treated with 5 IU of pregnant mare serum gonadotropin (Prospec, Rehovot, Israel) and 5 IU of human chorionic gonadotropin (hCG; Prospec) to induce superovulation of oocytes for assessment. Hormonally stimulated ovaries were removed postovulation and immediately placed in liquid nitrogen, prior to processing for mRNA and small RNA sequencing. RNA profiling was performed using gonadotropin-stimulated ovaries to analyze mRNAs and miRNAs contributing to the clinical phenotypes of poor oocyte quantity and quality.
Oocytes with dispersed chromosomes or spindle disassembly were scored as abnormal.
2.3 | RNA sequencing for mRNA and small RNA expression Hormonally stimulated ovaries were collected from the mice postovulation, and total RNA was extracted using TRIzol (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. The purity and integrity of the extracted RNA were evaluated using a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and Agilent 2100 bioanalyzer (Agilent Technologies, Amstelveen, The Netherlands). All samples showed high purity (optical density [OD] 260 /OD 280 > 1.80) and integrity (RNA integrity number > 7.0). Sequencing was performed using the Illumina NextSeq 500 platform following the vendor's instruction by E-biogen, Inc.
(Seoul, Korea). A fold-change value of >2.0 and a p value of <0.01 were used as thresholds to identify differentially expressed genes.

| Integrative analysis of mRNA and microRNA expression profiles
Gene enrichment and functional annotation analysis for a significant probe list were performed using Gene ontology (GO) (http:// geneontology.org) and the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://kegg.jp) to identify the potential functions of differentially expressed genes (DEGs) in biological processes.
Statistical significance threshold of enrichment analysis was set at p < 0.05. The DEGs associated with four KEGG pathways were introduced into STRING (http://string-db.org, version 11.0) to build the protein-protein interaction network interaction (maximum number of interactors = 0 and confidence score > 0.4) (The UniProt Consortium, 2017). mRNAs and miRNAs that had been differently expressed between young and old mice with significance (p < 0.05) based on sequencing were included in the analysis. TargetScan 7.2 webtool (www.targetscan.org, accessed March 2018) was used to identify binding sites of critical miRNAs and DEGs based on a context++ model of miRNA targeting efficacy (Agarwal et al., 2015).

| Statistical analysis
Data are presented as mean AE SEM. The statistical significance of differences between two groups was determined by a Student's t test using GraphPad Prism version 8.4.0 (GraphPad Software, Inc., La Jolla, CA, USA). Differences were considered statistically significant at p < 0.05.

| Decline in oocyte quantity and quality with aging
To assess, the quantity and quality of oocytes under conditions of aging, young, and old mice were hormonally induced to undergo superovulation. Retrieved oocytes were observed under the microscope, and their quantity and quality were assessed (Figure 1a). The numbers of total retrieved oocytes and mature metaphase II (MII) oocytes with normal chromosomes and well-organized spindle alignments were significantly lower in old mice than in young mice ( Figure 1b,c). These results indicate that aging is a risk factor for poor oocyte quality and quality.

| Integrative analysis of differentially expressed mRNAs and miRNAs
Further enrichment analysis using the TargetScan webtool identified possible direct interactions between differentially expressed mRNAs and miRNAs. We found that 24 DEGs harbored binding sites for the eight miRNAs, as summarized in Tables 3 and 4. Of note, the majority of interacting miRNAs, including miR-200b-3p, miR-741-3p, miR-200a-3p, and miR-141-3p, harbored binding sites for DEGs involved in folliculogenesis and steroidogenesis.
F I G U R E 1 Quantity and quality of mouse oocytes. Oocytes retrieved from young (n = 10) and old (n = 10) mice at 18 h after hCG injection. (a) Representative images of retrieved oocytes, as observed under the microscope (blue fluorescence: chromosome; green fluorescence: spindle). Number of retrieved oocytes (b) and MII oocytes with normal spindle morphology and chromosomal alignment (c) from young and old mice. Data are presented as mean AE standard error of the mean. Statistical analysis was performed using the Students t test. Young: 12-week-old mice; old: 44-week-old mice T A B L E 1 Top 10 biological processes associated with differentially expressed mRNAs in the ovaries of young and old mice   (Boone et al., 1993;Cordeiro et al., 2018). Women with polycystic ovarian syndrome exhibited lower levels of apolipoprotein A1 than did normal women (Couto Alves et al., 2017). In addition, arachidonic acid serves as a cell-signaling intermediate responsible for modulating cyclic AMP activation and PI3K/Akt, which regulate ovarian cellular growth and proliferation (Hughes-Fulford et al., 2006;. The PI3K/Akt signaling pathway plays important roles in follicular growth and atresia. High PI3K/Akt activity is linked to a decline in the number of primordial follicles and ovarian aging due to the regulation of follicular recruitment by this pathway. In contrast, inhibition of PI3K/Akt accelerates apoptosis of granulosa cells and leads to premature ovarian failure (Zheng et al., 2012). Spatiotemporal control of PI3K/Akt activation and function may contribute to the maintenance of the primordial follicle pool and oocyte maturation.
Ovarian aging alters not only ovarian follicular activation but also steroidogenesis. As they age, women produce lower levels of estradiol and other estrogen hormones (Gosden & Faddy, 1994). Dysregulation of feedback in the pituitary gland to regulate the level of folliclestimulating hormone affects ovarian follicular development and compromises the chances of pregnancy (Stilley & Segaloff, 2018). The expression of growth differentiation factor 9 (Gdf9) and bone morphogenetic protein 15 (Bmp15) can serve as a predictor of ovarian aging.
Age-related decline in the expression of these two genes is associated with poor ovarian response to ovarian hyperstimulation, resulting in poor oocyte quantity and quality (Gong et al., 2021). Interestingly, our analyses revealed that Bmp 15 harbors two binding sites for miR-200b-3p and one for miR-741-3p. However, functional analysis of the interaction between them is necessary. Our RNA sequencing analyses predicted that altered gene expression influences ovarian aging, leading to decreased oocyte quality.
Furthermore, GO analysis revealed several genes involved in cell differentiation, proliferation, and the meiotic cell cycle. Both 2 0 -5 0 oligoadenylate synthetase 1D (Oas1d) and estrogen receptor 2 (Esr2), which are known to be involved in folliculogenesis and oocyte growth, harbor binding sites for miR-141-3p and miR-200a-3p. Decreased expression of Oas1d has been shown in mouse oocytes exposed to cyclophosphamide which induces oxidative damage to DNA structural breaks and mutations (Kim & You, 2020;Spears et al., 2019). This interaction between these two mRNAs and two miRNAs may contribute to the maintenance of normal ovarian function and may be T A B L E 2 List of microRNAs altered in old compared to young mice deregulated during the aging process. Angiotensinogen (Agt), one of the identified DEGs that interacts with miR-200b-3p, miR-741-3p, and miR-881-3p, plays a role in the local renin-angiotensin system in the ovary. Agt has been shown to participate in folliculogenesis, steroidogenesis, oocyte maturation, and ovulation (Reis et al., 2011;Yoshimura, 1997).
T A B L E 4 Binding sites between downregulated mRNAs and microRNAs There are three limitations to our current study. First, we were unable to perform functional validations of our findings. Functional analysis of DEGs will be necessary to develop strategies or approaches for preventing reproductive dysfunction associated with ovarian aging.
Second, gonadotropin ovarian stimulation can influence gene expression in mouse ovaries (Marshall & Rivera, 2018). Older women undergoing IVF respond poorly to ovarian stimulation protocols, and their oocytes are often of lower yield and quality compared with those from younger women (Conforti et al., 2019;Lee et al., 2012). Transcriptomic analysis of gonadotropin-stimulated ovaries can provide insight into the molecular processes contributing to poor response to gonadotropin stimulation for aged women receiving IVF. Third, the ovaries were removed immediately after ovulation at the time of analysis in this study, and hence, they were in the luteal phase. Therefore, they could not have been responsible for folliculogenesis and oocyte maturation. Over the reproductive lifespan of a female, the number of follicles and oocytes decreases (Llibertos et al., 2021). It is thought that there was a difference in the expression of folliculogenesis-related genes due to the age-related decrease in the number of follicles.
In summary, we determined age-related changes in the genetic regulation of ovarian function. Integrative analysis showed that ovarian aging can alter miRNA-mediated epigenetic modifications involved in the regulation of folliculogenesis, oocyte growth, and steroidogenesis, leading to decreased oocyte quality.

ACKNOWLEDGMENT
This work was supported by a project grant from the Korea Institute of Oriental Medicine (KSN2021240).

CONFLICT OF INTEREST
Authors declare no conflict of interest for this article.

DATA AVAILABILITY STATEMENT
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
RNA seq and small RNA seq data have been registered in the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/ Traces/study/?acc=PRJNA760963).